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Probabilistic Programming
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Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.
<span style="color:grey; opacity: 0.6">( Image credit: Michael Betancourt )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JulienNonin/auto-diff-variational-inference
https://github.com/Mind23-2/MindCode-5/tree/main/RAOD
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
https://github.com/MindSpore-paper-code-2/code2/tree/main/RAOD
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
https://github.com/TuringLang/Bijectors.jl
https://github.com/UnofficialJuliaMirrorSnapshots/Bijectors.jl-76274a88-744f-5084-9051-94815aaf08c4
https://github.com/acr42/Neural-Variational-Knowledge-Graphs
https://github.com/aleatory-science/numpyro
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
https://github.com/allnightlight/ConditionalWassersteinAutoencoderPoweredBySinkhornDistance
https://github.com/anhinga/fluid
Dataflow Matrix Machines as a Generalization of Recurrent Neural Networks
https://github.com/babylonhealth/multiverse
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
https://github.com/bradleygramhansen/pyfo
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
https://github.com/code-implementation1/Code6/tree/main/RAOD
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
https://github.com/damast93/gaussianinfer
Compositional Semantics for Probabilistic Programs with Exact Conditioning
https://github.com/findaheng/Scenic
Scenic: A Language for Scenario Specification and Scene Generation
https://github.com/google/edward2
Simple, Distributed, and Accelerated Probabilistic Programming
https://github.com/hongseok-yang/probprog19
https://github.com/jaberkow/TensorFlowSinkhorn
https://github.com/jj-zhu/leibniz-ss-2021
https://github.com/kampta/pytorch-distributions
https://github.com/kesslerlib/kessler
Spacecraft Collision Risk Assessment with Probabilistic Programming
https://github.com/kirthevasank/mps
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
https://github.com/lukasadam/piVAE
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
https://github.com/modichirag/gsm-vi
https://github.com/mukehvier/tensorflow-prob
https://github.com/neurips2020submission7954/numpyro
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
https://github.com/nicola-decao/s-vae
https://github.com/nicola-decao/s-vae-tf
https://github.com/plai-group/pyprob
Inference Compilation and Universal Probabilistic Programming
https://github.com/probcomp/cgpm
https://github.com/probprog/pyprob
Inference Compilation and Universal Probabilistic Programming
https://github.com/rmascarenhas/foppl
https://github.com/s-hironobu/pg_plan_inspector
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
https://github.com/storopoli/Bayesian-Julia
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
https://github.com/tensorflow/probability
https://github.com/wsgharvey/pyro-infcomp-examples
Inference Compilation and Universal Probabilistic Programming
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.